Single-cell RNA-Seq Analysis
Timothy Tickle and Brian Haas
October 12, 2016
We Know Tissues are Complex
We can Separate Populations of Cells
We can Separate Populations of Cells
Heterogeneity can Help Describe Cell Type
Heterogeneity can Help Describe Cell Type
Heterogeneity can Describe Phenotypes
Heterogeneity can Describe Phenotypes
Before We Get Started
- Single-cell RNA-Seq analysis is developing.
- Give you a feel for the data.
- Walk different analysis in a couple data sets.
- There is a vivid diversity of methodology
- These techniques will grow as the field does.
- Why these tools were chosen.
- This is a guided conversation through scRNA-Seq Analysis
- Breadth and targeted depth
- If you have expertise to add, let's have that conversation!
Before We Get Started
- This is VERY hands-on.
- Much can be applied to other analyses.
- Strengthen those R ninja skills!
- If you need, cut and pasting is available (cut_and_paste.txt).
- There will many cute corgi pictures.
What We Will Attempt to Cover
Section 1
Part 1: Overview of laboratory prep and sequence analysis.
Part 2: Characteristics of expression data and QC.
Section 2
Part 1: Plotting Single Cell RNA-Seq data.
Part 2: Evaluating and defining cell populations.
Welcome to Section 1
- Overview of assays (SmartSeq2, Dropseq, 10X).
- Overview of sequence data (SmartSeq2, Dropseq, 10X).
- Overview of Dropseq sequence analysis pipeline.
- Overview of 10X sequence analysis pipeline.
- Sequence level quality control.
- What does single cell expression data look like and why?
- Introduction to RStudio.
- Initial data exploration.
- Quality control for expression matrices.
- Filtering genes and cells.
- Data analysis using UMIs.
Overview of assays
SmartSeq2
SmartSeq2: Description
- Developed for single cell but can performed using total RNA.
- Selects for poly-A tail.
- Full transcript assay.
- Uses template switching for 5' end capture.
- Standard illumina sequencing.
- Off-the-shelf products.
- Hundreds of samples.
- Often do not see UMI used.
SmartSeq2: Assay Overview
- Poly-A capture with 30nt polyT and 25nt 5' anchor sequence.
- RT adding untemplated C
- Template switching
- Locked Nucleic Acid binds to untemplated C
- RT switches template
- Preamplification / cleanup
- DNA fragmentation and adapter ligation together.
- Gap Repair, enrich, purify
SmartSeq2: Equipment
Dropseq
Dropseq: Description
- Moved throughput from hundreds to thousands.
- Droplet-based processing using microfluidics
- Nanoliter scale aqueous drops in oil.
- 3' End
- Bead based (STAMPs).
- Single-cell transcriptomes attached to microparticles.
- Cell barcodes use split-pool synthesis.
- Uses UMI (Unique Molecular Identifier).
- RMT (Random Molecular Tag).
- Degenerate synthesis.
Dropseq: Assay Overview
Dropseq: Assay and UMI Construction
Macosko et al. 2015
Dropseq: Sequencing Overview
Macosko et al. 2015
Dropseq: Microfluidics
Source: http://mccarrolllab.com/dropseq
Dropseq: Equipment
Dropseq: Pointers
- Droplet-based assays can have leaky RNA.
- Before library generation wash off any medium (inhibits library generation).
- Adding PBS and BSA (0.05-0.01%) can protect the cell.
- Too much produces a residue making harvesting the beads difficult.
- Filter all reagent with a 80 micron strainer before microfluidics.
- Some purchased devices add a hydrophobic coating.
- Can deteriorate (2 months at best).
- Recoating does work (in-house).
10X: Massively Parallel Sequencing
10X: Description
- Droplet-based, 3' mRNA.
- GEM (Gel Bead in Emulsion)
- Standardized instrumentation and reagents.
- More high-throughput scaling to tens of thousands.
- Less processing time.
- Up to 50% capture rate.
- Cell Ranger software is available for install.
10X: Assay Overview
Zheng et al.
10X: Sequences
Zheng et al.
- 14nt barcode, 10nt UMI, 30nt polyT + sequencing adaptor and primers.
10X: Equipment
Comparing Assays
Comparing Assays
Based on ERCC spike-ins.
Accuracy: How well the abundance levels correlated with known spiked-in amounts.
Sensitivity: Minimum number of input RNA molecules required to detect a spike-in.
Comparing Assays
Svensson et al. 2016
Comparing Assays
- Different assays have different throughput.
- SmartSeq2 < Dropseq < 10X
- SmartSeq2 is full transcript.
- Plate-based methods get lysed in wells and so do not leak.
- Droplet-based can have leaky RNA.
- In Dropseq assays RT happens outside the droplets
- Can use harsher lysis buffers.
- 10X needs lysis buffers compatible with the RT enzyme.
- 10X is more standardized and comes with a pipeline.
- Dropseq is more customizable but more hands-on.
- Cost per library varies greatly.
A Word on Sorting
- After disassociating cells cells can be performed.
- Know your cells, are they sticky, are they big?
- Select an appropriate sized nozzle.
- Don't sort too quickly (1-2k cells per second or lower)
- the slower the more time cells sit in lysis after sorting
- 10 minutes max in lysis (some say 30 minutes)
- Calibrate speed of instrument with beads
- Check alignment every 5-6 plates
- Afterwards spin down to make sure cells are in lysis buffer
- Flash freeze
- Chloe Villani on sorting https://youtu.be/HiKY0eHDhIs
Sequence Pipeline Overviews
Starting Sequences
Assays have Different Formats
Smartseq2: Pipeline Overview
Dropseq: Pipeline Overview
- Common functionality: trimming, alignment, generating count matrix.
- Adds book keeping for cell barcodes and UMIs, bead error detection, cell barcode collapsing, UMI collapsing.
Dropseq: Further Help
10X: Pipeline Overview
- Steps very similar to the Dropseq pipeline.
10X: Further Help
Sequence Level Quality Control
- Much of the QC that is performed is using traditional tools.
Section Summary
- Single-cell RNA-Seq is a diverse ecosystem of assays.
- Each assay has pros and cons.
- Sequences derived from these assays are complex and vary.
- Different pipelines are needed to address different sequence formats.
- Common steps like aligning, QC, and reads counting are in common.
Let's Describe scRNA-Seq Count Data
We Start with a Matrix of Expression
Genes Have Different Distributions
Genes Have Different Distributions
Genes Have Different Distributions
Genes Have Different Distributions
Genes Have Different Distributions
Underlying Biology
Zero inflation.
- Drop-out event during reverse-transcription.
- Genes with more expression have less zeros.
- Complexity varies.
Transcription stochasticity.
- Transcription bursting.
- Coordinated transcription of multigene networks.
- Overdispersed counts.
Higher Resolution.
- More sources of signal
- Cell cycle
Karchenko et al.
Data analysis when using UMIs
Read Counts
Counts by UMIs
Summary of the Data
- We are still understanding scData and how to apply it.
- Data can be NOT normal.
- Data can be Zero-inflated.
- Data can be very noisy.
- Cells vary in library complexity.
- Keeping these characteristics in analysis assumptions.
- Trend to filter more conservatively with UMIs.
Introduction to RStudio
Motivation: Why Am I Using R?
- A lot of method development is happening in R.
- Free / open source / open science.
- Many supplemental computational biology packages.
- Data science is an art.
- Data often requires one to create and manipulate analysis.
- This will allow you to experience key concepts in analysis.
RStudio (IDE)
Initial data exploration
Seurat
Seurat: Today's Data
- Using data provided by 10X (2700 cells).
- Peripheral blood mononuclear cells (PBMCs).
- Any peripheral blood cell with a round nucleus.
- Lymphocytes (T cells, B cells, NK cells)
- Monocytes
- Complex mix cell populations used in immunity.
Logistics
- R allows methodology written by others to be imported.
- Leverage other code.
- Make your code available to others.
# Load libraries
library(dplyr) # Dataframe manipulation
library(Matrix) # Sparse matrices
library(useful) # Corner function
library(vioplot) # Violin pots
library(scater) # Single Cell QC
library(scde) # Differential Expression
library(org.Hs.eg.db) # Gene name manipulation
library(Seurat) # Single cell General Analysis
Representing Sparse Matrices
# Load 10X data
pbmc.10X <- Read10X("./data/filtered_gene_bc_matrices/hg19")
# Memory use as a sparse matrix
object.size(pbmc.10X)
38715120 bytes
# Memory use as a dense matrix
# 18 X more
object.size(as.matrix(pbmc.10X))
709264728 bytes
How are Sparse Representations Made?
- Sparse Matrix: A matrix where most of the elements are 0.
- Dense Matrix: A matrix where most elements are not 0.
- In this case, underlying data structure is a coordinate list.
2D Arrays vs Coordinate List
Create a Seurat Object
# Expected raw counts (non-normalized data)
# Can give log transformed data but do not transform in setup method
pbmc.seurat <- new("seurat", raw.data=pbmc.10X)
What is in a Seurat Object?
# Display the internal pieces of the Seurat Object
slotNames(pbmc.seurat)
[1] "raw.data" "data" "scale.data"
[4] "var.genes" "is.expr" "ident"
[7] "pca.x" "pca.rot" "emp.pval"
[10] "kmeans.obj" "pca.obj" "gene.scores"
[13] "drop.coefs" "wt.matrix" "drop.wt.matrix"
[16] "trusted.genes" "drop.expr" "data.info"
[19] "project.name" "kmeans.gene" "kmeans.cell"
[22] "jackStraw.empP" "jackStraw.fakePC" "jackStraw.empP.full"
[25] "pca.x.full" "kmeans.col" "mean.var"
[28] "imputed" "mix.probs" "mix.param"
[31] "final.prob" "insitu.matrix" "tsne.rot"
[34] "ica.rot" "ica.x" "ica.obj"
[37] "cell.names" "cluster.tree" "snn.sparse"
[40] "snn.dense" "snn.k"
What is in a Seurat Object?
[1] "raw.data" "data" "scale.data"
[4] "var.genes" "is.expr" "ident"
[7] "pca.x" "pca.rot" "emp.pval"
[10] "kmeans.obj" "pca.obj" "gene.scores"
[13] "drop.coefs" "wt.matrix" "drop.wt.matrix"
[16] "trusted.genes" "drop.expr" "data.info"
[19] "project.name" "kmeans.gene" "kmeans.cell"
[22] "jackStraw.empP" "jackStraw.fakePC" "jackStraw.empP.full"
[25] "pca.x.full" "kmeans.col" "mean.var"
[28] "imputed" "mix.probs" "mix.param"
[31] "final.prob" "insitu.matrix" "tsne.rot"
[34] "ica.rot" "ica.x" "ica.obj"
[37] "cell.names" "cluster.tree" "snn.sparse"
[40] "snn.dense" "snn.k"
What is in a Seurat Object?
Raw sparse matrix
head(pbmc.seurat@raw.data)
6 x 2700 sparse Matrix of class "dgTMatrix"
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 1 . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . .
FAM138A . . .
OR4F5 . . .
RP11-34P13.7 . . .
RP11-34P13.8 . . .
AL627309.1 . . .
What is in a Seurat Object?
- var.genes Variable genes across cells
- data.info Misc info including complexity (nGene)
- cell.names Column (cell) names
- gene.names Row (gene) names
?seurat
Always Look at Your Data
Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. -– Atul Butte
What are Our Genes?
# Gene names (row names)
head(row.names(pbmc.seurat@raw.data))
[1] "MIR1302-10" "FAM138A" "OR4F5" "RP11-34P13.7"
[5] "RP11-34P13.8" "AL627309.1"
length(row.names(pbmc.seurat@raw.data))
[1] 32738
What are Our Cells?
# Column names
# Sample / Cell names / Barcodes
head(colnames(pbmc.seurat@raw.data))
[1] "AAACATACAACCAC" "AAACATTGAGCTAC" "AAACATTGATCAGC" "AAACCGTGCTTCCG"
[5] "AAACCGTGTATGCG" "AAACGCACTGGTAC"
length(colnames(pbmc.seurat@raw.data))
[1] 2700
How to Show Counts?
# Only the corner
# The full data will be too large to see
corner(as.matrix(pbmc.seurat@raw.data))
How to Show Counts?
AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG
MIR1302-10 0 0 0 0
FAM138A 0 0 0 0
OR4F5 0 0 0 0
RP11-34P13.7 0 0 0 0
RP11-34P13.8 0 0 0 0
AAACCGTGTATGCG
MIR1302-10 0
FAM138A 0
OR4F5 0
RP11-34P13.7 0
RP11-34P13.8 0
Complexity of Libraries
- Very important metadata.
- How many genes had a count of more than 1 in a cell.
- Simplest, most common definition.
How Many Expressed Genes?
# Plot genes per cell How many genes expressed per cells
complexity.per.cell <- apply(pbmc.seurat@raw.data, 2, function(x) sum(x > 0))
# Mean count per cell.
mean.count.per.cell <- apply(pbmc.seurat@raw.data, 2, function(x) mean(x))
# Gene prevalence
gene.prevalence <- apply(pbmc.seurat@raw.data, 1, function(x) sum(x > 0))
How Many Expressed Genes?
# Plot genes per cell How many genes expressed per cell
vioplot(complexity.per.cell)
stripchart(complexity.per.cell, add = TRUE, vertical = TRUE, method = "jitter",
jitter = 0.3, pch = 19)
abline(h = 200, col = "red")
abline(h = 2500, col = "blue")
title("Study Complexity")
axis(side = 1, at = 1, labels = c("Study"))
How Many Expressed Genes?
- Shown complexity of all cells in a study.
- Wide range of complexity.
- Prefiltered data at red line.
- Will define points about the blue line as outlier.
Box Plots vs Violin Plots
Boxplot:
Robust representation of a distribution using quantiles
Violin Plot:
Box plot with kernel density plot mirrored on sides.
Identifying Outliers?
- Cells that are unusually simple (or no counts)
- Cells that are unusually complex (doublets?)
plot(complexity.per.cell, mean.count.per.cell)
abline(v = 200, col = "red")
abline(h = log2(4))
Identifying Outliers?
- We will filter in a standard way, we are just describing the data.
- Red line shows a point of prefiltering.
Identifying Noise?
hist(log2(gene.prevalence))
Identifying Noise?
- People tend to filter very conservatively.
- Remember this is in log space.
Filter Cells: Removing the Outlier Cells
- Genes must be in 3 cells with alteast 200 genes.
- Scaled by 1000 (Total Sum Scaling)
pbmc.seurat <- Setup(pbmc.seurat, min.cells = 3, min.genes = 200, do.logNormalize = TRUE,
total.expr = 10000, project = "Tutorial")
[1] "Performing log-normalization"
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Expression: Definition
Population based RNA-Seq
- Different ways to move from counts to expression.
- EM algorithms (RSEM)
- TPM with TMM normalization.
- Control for transcript length and force the depth to be 106.
- Trimmed means weighted focusing on invariant genes through samples.
Today we are using
- log2( ( cell_counts / cell_sum ) * 1000 + 1 )
- Account for sequencing depth.
Filter Genes: Averages are Less Useful
Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable. –Bobby Bragan
Filter Genes: Averages are Less Useful
Filter Genes: Using Prevalence
Filtering with gene prevalence:
How many times a gene's count is greater than or equal to an expression threshold throughout cells.
Filter Genes: Using Prevalence
Filtering Using Metadata
What is Metadata?
Other information that describes your measurements.
- Patient information.
- Life style (smoking), Patient Biology (age), Comorbidity
- Study information.
- Treatment, Cage, Sequencing Site, Sequencing Date
- Sequence QC on cells.
- Useful in filtering.
Filter Cells: Removing the Outlier Cells
- Outlier cells are not just measured by complexity
- Useful Metrics
- Percent Reads Mapping
- Percent Mitochondrial Reads
- Presence of marker genes
- Intergenic/ exonic rate
- 5' or 3' bias
- other metadata …
- Useful Tools
- Picard Tools and RNASeQC
- Useful Metrics
Seurat: Filtering on Metadata
- Filtering on mitochondrial reads in Seurat.
# Get gene names
mito.gene.names <- grep("^MT-", rownames(pbmc.seurat@data), value=TRUE)
# Get TSS normalized mitochodrial counts
col.total.counts <- Matrix::colSums(expm1(pbmc.seurat@data))
mito.percent.counts <- Matrix::colSums(expm1(pbmc.seurat@data[mito.gene.names, ]))/col.total.counts
# Add to seurat object as a metadata
pbmc.seurat <- AddMetaData(pbmc.seurat, mito.percent.counts, "percent.mitochodrial")
Seurat: Filtering on Metadata
- Plot current metadata in Seurat Object.
- Number gene.
- Number UMI.
- Percent mitochondrial counts.
Seurat: Filtering on Metadata
Seurat: Filtering on Metadata
GenePlot(pbmc.seurat, "nUMI", "percent.mitochodrial")
Seurat: Filtering on Metadata
- Outlier percent mitochondria are very low expression.
- Very high expression low percent mitochondrial reads.
Seurat: Filtering on Metadata
Seurat: Filtering on Metadata
dim(pbmc.seurat@data)
[1] 13714 2700
pbmc.seurat <- SubsetData(pbmc.seurat, subset.name = "nGene", accept.high = 2500)
pbmc.seurat <- SubsetData(pbmc.seurat, subset.name = "percent.mitochodrial", accept.high = 0.05)
dim(pbmc.seurat@data)
[1] 13714 2638
Saving as an R Object
Saving the Seurat object
- Contains all manipulation so far.
- Can be loaded or shared.
- Does not contain environment.
# How to save the intact object.
save(pbmc.seurat, file = "seurat_tutorial.Robj")
# How to retrieve the intact object.
load("seurat_tutorial.Robj")
Saving as Text Files
You may need to export data to import into other applications.
# Log-scale expression matrix
write.table(as.matrix(pbmc.seurat@data), file = "seurat_data.txt")
# Study metadata
write.table(pbmc.seurat@data.info, file = "seurat_metadata.txt")
# What is the metadata so far
head(pbmc.seurat@data.info)
Scater: Complex QC Plotting
Scater: Motivation
- Clinical studies are often rich with metadata.
- Most differential expression methodology is T-test.
- Much like using covariates in linear regression to control here we stratify by metadata to describe the impact of the metadatum.
- More than what we are covering here.
- Online tutorial Click Here
Scater: Skipping Ahead
# Load Data
data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data=sc_example_cell_info)
rownames(pd) <- pd$Cell
example_sceset <- newSCESet(countData=sc_example_counts, phenoData=pd)
keep_feature <- rowSums(exprs(example_sceset)) > 0
example_sceset <- example_sceset[keep_feature,]
example_sceset <- calculateQCMetrics(example_sceset, feature_controls = 1:40)
Scater: Describe the Data
Small data set of 40 cells.
- Constructed metadata
- Metadata including
- Mutation status (positive, negative)
- Cell Cycle (S, G0, G1, G2M)
- Treament (treat1, treat2)
Scater: What is the Data?
corner(sc_example_counts)
Cell_001 Cell_002 Cell_003 Cell_004 Cell_005
Gene_0001 0 123 2 0 0
Gene_0002 575 65 3 1561 2311
Gene_0003 0 0 0 0 1213
Gene_0004 0 1 0 0 0
Gene_0005 0 0 11 0 0
Scater: What is the info?
corner(sc_example_cell_info)
Cell Mutation_Status Cell_Cycle Treatment
Cell_001 Cell_001 positive S treat1
Cell_002 Cell_002 positive G0 treat1
Cell_003 Cell_003 negative G1 treat1
Cell_004 Cell_004 negative S treat1
Cell_005 Cell_005 negative G1 treat2
Scater: Stratifying for Metadata
plot(example_sceset, block1 = "Mutation_Status", block2 = "Treatment",
colour_by = "Cell_Cycle", nfeatures = 300, exprs_values = "counts")
Scater: Stratifying for Metadata
Scater: Stratifying for Metadata
plotExpression(example_sceset, rownames(example_sceset)[1:6],
x = "Mutation_Status", exprs_values = "exprs", colour = "Treatment")
Scater: Stratifying for Metadata
Scater: Interactive Exploration
scater_gui(example_sceset)
Section Summary
- scRNA-Seq is complex and can NOT be treated as population based measurements.
- Cell complexity is an important metadatum for QC.
- Conservative gene filtering based on gene count and prevalence is often used.
- Cells can be filtered by complexity and also other metadatum.
Where are we at?
We have now
- Loaded in data into R.
- Explored and plotted the data for QC.
- Filtered on counts and metadata.
Next
- Focus on plotting Genes and Cells.
- View cell groups to later use in DE.
Let's Get Started!
Welcome to section 2!
- Using Seurat to plot genes
- Batch effects and study confounding
- Dimensionality reduction and plotting (PCA, t-SNE).
- Plotting cells.
- Moving from clusters to populations of cells.
- Differential Expression (SCDE).
- Pathway analysis.
- Overview of available methodology.
Using Seurat to plot genes
Seurat: Viewing Specific Genes
- Check the identity of the cells!!!
- Notice many zeros.
VlnPlot(pbmc.seurat, c("GAPDH"))
Seurat: Viewing Specific Genes
- Check the identity of the cells!!!
- Notice many zeros.
Seurat: Plotting Genes vs Genes
# Plot a gene vs a gene
GenePlot(pbmc.seurat, "CD79A", "CD79B", cex.use = 1)
Seurat: Plotting Genes vs Genes
GenePlot(pbmc.seurat, "CD79A", "CD79B", cex.use = 1)
Single Cell RNA-Seq and Batch Affects
Single Cell RNA-Seq and Batch Affects
What is Confounding?
Confounding by Design
Normalization and Batch Affect Correction
- The nature of scRNA-Seq assays can make them prone to confounding with batch affects.
- Normalization and batch affect correction can help.
- Some are moving away from relying on a specific method.
- Exploring the idea of combining or selecting from a collection of normalization or correction methods best for a specific study.
- Some believe UMI based analysis need not be normalized between samples given the absolute count of the molecules are being reported.
- Be careful not to remove biological signal with good experimental design (avoiding confounding by design).
Seurat and Batch Affect Correction
- Using linear models one can regress covariates.
- scale.data hold the residuals after regressing (z-scored)
- Dimensionality reduction and clustering.
- We use metadata we have.
- One could imagine creating a metadata for cell cycle.
Seurat and Batch Affect Correction
pbmc.seurat <- RegressOut(pbmc.seurat, latent.vars = c("nUMI", "percent.mitochodrial"))
[1] "Regressing out nUMI"
[2] "Regressing out percent.mitochodrial"
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[1] "Scaling data matrix"
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Scone: Data
- 65 human cells
- Cultured neural progenitor cells (“NPC”) from pluripotent stem cells.
- Primary cortical samples at 16 and 21 gestation weeks.
- 21 gestation weeks then cultered for 3 weeks.
- Pollen et al 2014
Scone: Batch Correction
set.seed(6473)
library(scone)
library(RColorBrewer)
library(scRNAseq)
data(fluidigm)
# Set assay to RSEM estimated counts
assay(fluidigm) = assays(fluidigm)$rsem_counts
Scone: Which Metadata?
- Which metadata are we using?
metadata(fluidigm)$which_qc
Scone: Which Metadata?
[1] "NREADS" "NALIGNED"
[3] "RALIGN" "TOTAL_DUP"
[5] "PRIMER" "INSERT_SZ"
[7] "INSERT_SZ_STD" "COMPLEXITY"
[9] "NDUPR" "PCT_RIBOSOMAL_BASES"
[11] "PCT_CODING_BASES" "PCT_UTR_BASES"
[13] "PCT_INTRONIC_BASES" "PCT_INTERGENIC_BASES"
[15] "PCT_MRNA_BASES" "MEDIAN_CV_COVERAGE"
[17] "MEDIAN_5PRIME_BIAS" "MEDIAN_3PRIME_BIAS"
[19] "MEDIAN_5PRIME_TO_3PRIME_BIAS"
Scone: Accessing Metadata
head(colData(fluidigm))
head(colData(fluidigm)$Coverage_Type)
Scone: Logistics
- Cells were sequenced twice.
- Removing low coverage replicates.
- Removing undetected genes.
# Preliminary Sample Filtering: High-Coverage Only
is_select = colData(fluidigm)$Coverage_Type == "High"
fluidigm = fluidigm[,is_select]
# Retain only detected transcripts
fluidigm = fluidigm[which(apply(assay(fluidigm) > 0,1,any)),]
Scone: Batches
Scone: Batches
Scone: Related Metrics
Scone: Strongest Signal
Scone: Filtering
- Filtering on prevalance at a certain level of expression.
# Initial Gene Filtering: Select "common" transcripts based on proportional criteria.
num_reads = quantile(assay(fluidigm)[assay(fluidigm) > 0])[4]
num_cells = 0.25*ncol(fluidigm)
is_common = rowSums(assay(fluidigm) >= num_reads ) >= num_cells
Scone: Filtering
- We will be pulling in house keeping genes for the filtering.
data(housekeeping)
hk = intersect(housekeeping$V1,rownames(assay(fluidigm)))
head(housekeeping$V1)
[1] "AATF" "ABL1" "ACAT2" "ACTB" "ACTG1" "ACTN4"
Scone: Filtering
# Metric-based Filtering
mfilt = metric_sample_filter(assay(fluidigm),
nreads=colData(fluidigm)$NREADS,
ralign=colData(fluidigm)$RALIGN,
gene_filter=is_common,
pos_controls=rownames(fluidigm) %in% hk,
zcut=3, mixture=FALSE,
plot=FALSE)
Scone: Filtering
Scone: Filtering
# Simplify to a single logical
mfilt = !apply(simplify2array(mfilt[!is.na(mfilt)]),1,any)
Scone: Filtering
- Apply cell filtering and filter on highly expressed gene.
# Cell filter
goodDat = fluidigm[,mfilt]
# Final Gene Filtering: Highly expressed in at least 5 cells
num_reads = quantile(assay(fluidigm)[assay(fluidigm) > 0])[4]
num_cells = 5
is_quality = rowSums(assay(fluidigm) >= num_reads ) >= num_cells
Scone: Workflows Inputs
# Expression Data (Required)
expr = assay(goodDat)[is_quality,]
# Biological Origin - Variation to be preserved (Optional)
bio = factor(colData(goodDat)$Biological_Condition)
Scone: Workflows Inputs
# Processed Alignment Metrics - Variation to be removed (Optional)
qc = colData(goodDat)[,metadata(goodDat)$which_qc]
ppq = scale(qc[,apply(qc,2,sd) > 0],center = TRUE,scale = TRUE)
# Positive Control Genes - Prior knowledge of DE (Optional)
poscon = intersect(rownames(expr),strsplit("ALS2, CDK5R1, CYFIP1, DPYSL5, FEZ1, FEZ2, MAPT, MDGA1, NRCAM, NRP1, NRXN1, OPHN1, OTX2, PARD6B, PPT1, ROBO1, ROBO2, RTN1, RTN4, SEMA4F, SIAH1, SLIT2, SMARCA1, THY1, TRAPPC4, UBB, YWHAG, YWHAH",split = ", ")[[1]])
Scone: Workflows Inputs
# Negative Control Genes - Uniformly expressed transcripts (Optional)
negcon = intersect(rownames(expr),hk)
Scone: User Functions
- Total sum scaling
SUM_FN = function (ei)
{ # TSS
sums = colSums(ei)
eo = t(t(ei)*mean(sums)/sums)
return(eo)
}
Scone: User Functions
- Similar to TSS but with complexity.
EFF_FN = function (ei)
{ # Divided by complexity
sums = colSums(ei > 0)
eo = t(t(ei)*sums/mean(sums))
return(eo)
}
Scone: User Functions
scaling=list(none=identity, #do nothing
sum = SUM_FN, # User functions
eff = EFF_FN,
tmm = TMM_FN, # SCONE
uq = UQ_FN,
uqp = UQ_FN_POS,
fq = FQT_FN,
fqp = FQ_FN_POS,
deseq=DESEQ_FN,
deseqp=DESEQ_FN_POS)
Scone: Imputation Settings
# Simple FNR model estimation with SCONE::estimate_ziber
# fnr_out = estimate_ziber(x = expr, bulk_model = TRUE,
# pos_controls = rownames(expr) %in% hk,
# maxiter = 10000)
load("data/fnr_out.Robj")
Scone: Imputation Settings
## ----- Imputation List Argument -----
imputation=list(none=impute_null, # No imputation
expect=impute_expectation)
# Replace zeroes with expected expression level
Scone: Imputation Settings
- Passed into functions in the imputation list.
impute_args = list(p_nodrop = fnr_out$p_nodrop, mu = exp(fnr_out$Alpha[1,]))
Scone: Setting Up Params
params <- scone(expr,
imputation = imputation, impute_args = impute_args,
scaling=scaling,
qc=ppq, bio = bio,
# Negative controls for RUVg
# normalization and evaluation
ruv_negcon = negcon,
# Parameter Arguments
k_qc=3, k_ruv = 3,
adjust_bio="no",
run=FALSE)
Scone: Workflow Params
# Visualize output params object
head(params)
Scone: Workflow Params
apply(params,2,unique)
Scone: Updating for Imputation
- Removing some filtering for the imputed data.
is_screened = ((params$imputation_method == "expect") & (params$scaling_method %in% c("none","deseqp","uqp","fqp","eff")))
params = params[!is_screened,]
Scone: Filtered Params
head(params)
Scone: Run!
BiocParallel::register(BiocParallel::SerialParam())
res <- scone(expr,
imputation = imputation, impute_args = impute_args,
scaling=scaling,
qc=ppq, bio = bio,
ruv_negcon = negcon,
k_qc=3, k_ruv = 3,
adjust_bio="no",
eval_poscon = poscon, # Positive controls for evaluation
run=TRUE, params = params, # Additional params
eval_kclust = 2:6,stratified_pam = TRUE,
return_norm = "in_memory",
rezero = TRUE)
Scone: Output
- normalized_data: list of normalized expression data (log-scale).
- metrics: raw metrics for normalizations.
- scores: sorted priority of best workslows.
class:small-code
names(res)
[1] "normalized_data" "metrics" "scores" "params"
Scone: Biplot
Scone: Interactive!
sconeReport(scone_res = res,
qc = ppq,
bio = bio,
negcon = negcon, poscon = poscon)
Scone: Tutorial
If you are interested in reading more about Scone feel free to visit this tutorial.
Dimensionality reduction and plotting
Dimensionality Reduction and Ordination
- Start with many measurements (high dimensional).
- Want to reduce to a few features (lower-dimensional space).
- One way is to extract features based on capturing groups of variance.
- Another could be to preferentially select some of the current features.
- We have already done this.
- We need this to plot the cells in 2D (or ordinate them)
- In scRNA-Seq PC1 may be complexity.
PCA: in Quick Theory
- Eigenvectors of covariance matrix.
- Find orthogonal groups of variance.
- Given from most to least variance.
- Components of variation.
- Linear combinations explaining the variance.
PCA: in Quick Theory
PCA: in Practice
Things to be aware of.
- Data with different magnitudes will dominate.
- Zero center and divided by SD.
- (Standardized).
- Can be affected by outliers.
t-SNE: Nonlinear Dimensional Reduction
t-SNE: Collapsing the Visualization to 2D
t-SNE: What and Why?
PCA and t-SNE Together
Often t-SNE is performed on PCA components
- Liberal number of components.
- Removes mild signal (assumption of noise).
- Faster, on less data but, hopefully the same signal.
Plotting Cells
Select Variable Genes
- We are going to focus on highly variable genes.
- Average expression (X) and dispersion (SD)(Y).
- Bins genes (20).
- Z-score for dispersion.
- fxn.x and fxn.y allow one to change the measurements.
- Cut.offs are high and low, X and Y.
pbmc.seurat <- MeanVarPlot(pbmc.seurat,fxn.x=expMean,fxn.y=logVarDivMean,
x.low.cutoff=0.0125,x.high.cutoff=3,
y.cutoff=0.5,do.contour=FALSE,do.plot=FALSE)
Select Variable Genes
[1] "Calculating gene dispersion"
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Select Variable Genes
length(pbmc.seurat@var.genes)
[1] 1839
Seurat: Performing PCA
- Calculating PCA with the highly variable genes.
pbmc.seurat <- PCA(pbmc.seurat,pc.genes=pbmc.seurat@var.genes,do.print=FALSE)
Seurat: Performing PCA
# Calculate PCA projection
pbmc.seurat <- ProjectPCA(pbmc.seurat)
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[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1" "LYZ"
[7] "LST1" "AIF1" "S100A8" "FTH1" "TYMP" "LGALS2"
[13] "CFD" "FCER1G" "LGALS1" "CD68" "CTSS" "SERPINA1"
[19] "SAT1" "NPC2" "GRN" "CFP" "IFITM3" "COTL1"
[25] "IFI30" "PSAP" "SPI1" "CD14" "GPX1" "S100A11"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A" "RPL23A" "IL32"
[8] "RPL3" "RPL9" "RPS27" "CD3D" "LTB" "RPL21" "RPS3"
[15] "RPS6" "RPS15A" "RPL31" "CD3E" "RPL13A" "LDHB" "RPS25"
[22] "RPL30" "RPS12" "RPS18" "RPS23" "RPS29" "RPL27A" "RPL5"
[29] "EEF1A1" "AES"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA" "FGFBP2" "GNLY"
[8] "CTSW" "SPON2" "CCL4" "GZMH" "CCL5" "FCGR3A" "KLRD1"
[15] "XCL2" "CLIC3" "SRGN" "GZMM" "B2M" "CD247" "AKR1C3"
[22] "S100A4" "PRSS23" "TTC38" "S1PR5" "HCST" "IGFBP7" "ITGB2"
[29] "HOPX" "GPR56"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[6] "HLA-DQB1" "CD79B" "LINC00926" "LTB" "RPL32"
[11] "VPREB3" "RPL13A" "HLA-DRA" "RPL13" "RPS23"
[16] "RPS27" "RPL11" "RPS18" "RPL8" "RPS5"
[21] "HLA-DQA2" "RPLP2" "RPL12" "RPS2" "CD37"
[26] "FCER2" "CD74" "BANK1" "RPS12" "HLA-DRB1"
[1] ""
[1] ""
[1] "PC3"
[1] "RPL10" "RPS2" "RPL11" "RPL28" "RPL32" "RPL18A" "RPL12"
[8] "RPL19" "RPS6" "TMSB10" "RPS14" "RPL13" "RPS19" "RPS15"
[15] "RPL6" "RPL29" "RPLP1" "RPS3" "RPL15" "RPL26" "RPS4X"
[22] "EEF1A1" "RPS16" "RPS7" "GNB2L1" "RPL14" "RPL13A" "RPL3"
[29] "RPS18" "RPL8"
[1] ""
[1] "PF4" "PPBP" "SDPR" "SPARC"
[5] "GNG11" "HIST1H2AC" "GP9" "NRGN"
[9] "TUBB1" "RGS18" "CLU" "AP001189.4"
[13] "ITGA2B" "CD9" "PTCRA" "TMEM40"
[17] "CA2" "ACRBP" "MMD" "TREML1"
[21] "F13A1" "SEPT5" "PGRMC1" "MYL9"
[25] "TSC22D1" "MPP1" "CMTM5" "PTGS1"
[29] "SNCA" "RP11-367G6.3"
[1] ""
[1] ""
[1] "PC4"
[1] "CD3D" "LDHB" "IL7R" "RPS14" "CD3E" "VIM" "IL32"
[8] "RPL32" "RPS12" "NOSIP" "RPL28" "RPS25" "GIMAP7" "RPL11"
[15] "JUNB" "RPL13" "RPS3" "AQP3" "ZFP36L2" "FYB" "RPL10"
[22] "RGCC" "MAL" "FOS" "LEF1" "RPLP1" "CD2" "RPL35A"
[29] "RPL36" "RPS28"
[1] ""
[1] "CD79A" "HLA-DQA1" "CD79B" "MS4A1" "CD74"
[6] "HLA-DQB1" "HLA-DPB1" "HLA-DPA1" "HLA-DRB1" "TCL1A"
[11] "HLA-DRA" "LINC00926" "HLA-DRB5" "HLA-DQA2" "VPREB3"
[16] "HLA-DMA" "GZMB" "HLA-DMB" "HVCN1" "FCER2"
[21] "BANK1" "FGFBP2" "HLA-DOB" "PDLIM1" "FCRLA"
[26] "TSPAN13" "PRF1" "GNLY" "CD72" "EAF2"
[1] ""
[1] ""
[1] "PC5"
[1] "FCER1A" "LGALS2" "MS4A6A" "S100A8" "CLEC10A" "FOLR3"
[7] "GPX1" "CD14" "GSTP1" "ALDH2" "S100A12" "SERPINF1"
[13] "ID1" "CD1C" "GRN" "RNASE6" "GSN" "IER3"
[19] "CSF3R" "BLVRB" "RPL13" "ASGR1" "S100A9" "RNASE2"
[25] "VCAN" "LYZ" "SAT2" "QPCT" "CEBPD" "FCGR1A"
[1] ""
[1] "FCGR3A" "CDKN1C" "MS4A7" "HES4"
[5] "CKB" "RP11-290F20.3" "RHOC" "CTSL"
[9] "MS4A4A" "LILRA3" "SIGLEC10" "CTD-2006K23.1"
[13] "IFITM3" "HMOX1" "ABI3" "LRRC25"
[17] "IFITM2" "LILRB1" "BATF3" "PTP4A3"
[21] "CEBPB" "PILRA" "CSF1R" "HCK"
[25] "CXCL16" "VMO1" "C1QA" "TPPP3"
[29] "TCF7L2" "TNFSF10"
[1] ""
[1] ""
# Can plot top genes for top components
PrintPCA(pbmc.seurat, pcs.print = 1:2, genes.print = 5, use.full = TRUE)
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[1] ""
[1] ""
Seurat: Performing PCA
# Calculate PCA projection
pbmc.seurat <- ProjectPCA(pbmc.seurat)
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[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1" "LYZ"
[7] "LST1" "AIF1" "S100A8" "FTH1" "TYMP" "LGALS2"
[13] "CFD" "FCER1G" "LGALS1" "CD68" "CTSS" "SERPINA1"
[19] "SAT1" "NPC2" "GRN" "CFP" "IFITM3" "COTL1"
[25] "IFI30" "PSAP" "SPI1" "CD14" "GPX1" "S100A11"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A" "RPL23A" "IL32"
[8] "RPL3" "RPL9" "RPS27" "CD3D" "LTB" "RPL21" "RPS3"
[15] "RPS6" "RPS15A" "RPL31" "CD3E" "RPL13A" "LDHB" "RPS25"
[22] "RPL30" "RPS12" "RPS18" "RPS23" "RPS29" "RPL27A" "RPL5"
[29] "EEF1A1" "AES"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA" "FGFBP2" "GNLY"
[8] "CTSW" "SPON2" "CCL4" "GZMH" "CCL5" "FCGR3A" "KLRD1"
[15] "XCL2" "CLIC3" "SRGN" "GZMM" "B2M" "CD247" "AKR1C3"
[22] "S100A4" "PRSS23" "TTC38" "S1PR5" "HCST" "IGFBP7" "ITGB2"
[29] "HOPX" "GPR56"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[6] "HLA-DQB1" "CD79B" "LINC00926" "LTB" "RPL32"
[11] "VPREB3" "RPL13A" "HLA-DRA" "RPL13" "RPS23"
[16] "RPS27" "RPL11" "RPS18" "RPL8" "RPS5"
[21] "HLA-DQA2" "RPLP2" "RPL12" "RPS2" "CD37"
[26] "FCER2" "CD74" "BANK1" "RPS12" "HLA-DRB1"
[1] ""
[1] ""
[1] "PC3"
[1] "RPL10" "RPS2" "RPL11" "RPL28" "RPL32" "RPL18A" "RPL12"
[8] "RPL19" "RPS6" "TMSB10" "RPS14" "RPL13" "RPS19" "RPS15"
[15] "RPL6" "RPL29" "RPLP1" "RPS3" "RPL15" "RPL26" "RPS4X"
[22] "EEF1A1" "RPS16" "RPS7" "GNB2L1" "RPL14" "RPL13A" "RPL3"
[29] "RPS18" "RPL8"
[1] ""
[1] "PF4" "PPBP" "SDPR" "SPARC"
[5] "GNG11" "HIST1H2AC" "GP9" "NRGN"
[9] "TUBB1" "RGS18" "CLU" "AP001189.4"
[13] "ITGA2B" "CD9" "PTCRA" "TMEM40"
[17] "CA2" "ACRBP" "MMD" "TREML1"
[21] "F13A1" "SEPT5" "PGRMC1" "MYL9"
[25] "TSC22D1" "MPP1" "CMTM5" "PTGS1"
[29] "SNCA" "RP11-367G6.3"
[1] ""
[1] ""
[1] "PC4"
[1] "CD3D" "LDHB" "IL7R" "RPS14" "CD3E" "VIM" "IL32"
[8] "RPL32" "RPS12" "NOSIP" "RPL28" "RPS25" "GIMAP7" "RPL11"
[15] "JUNB" "RPL13" "RPS3" "AQP3" "ZFP36L2" "FYB" "RPL10"
[22] "RGCC" "MAL" "FOS" "LEF1" "RPLP1" "CD2" "RPL35A"
[29] "RPL36" "RPS28"
[1] ""
[1] "CD79A" "HLA-DQA1" "CD79B" "MS4A1" "CD74"
[6] "HLA-DQB1" "HLA-DPB1" "HLA-DPA1" "HLA-DRB1" "TCL1A"
[11] "HLA-DRA" "LINC00926" "HLA-DRB5" "HLA-DQA2" "VPREB3"
[16] "HLA-DMA" "GZMB" "HLA-DMB" "HVCN1" "FCER2"
[21] "BANK1" "FGFBP2" "HLA-DOB" "PDLIM1" "FCRLA"
[26] "TSPAN13" "PRF1" "GNLY" "CD72" "EAF2"
[1] ""
[1] ""
[1] "PC5"
[1] "FCER1A" "LGALS2" "MS4A6A" "S100A8" "CLEC10A" "FOLR3"
[7] "GPX1" "CD14" "GSTP1" "ALDH2" "S100A12" "SERPINF1"
[13] "ID1" "CD1C" "GRN" "RNASE6" "GSN" "IER3"
[19] "CSF3R" "BLVRB" "RPL13" "ASGR1" "S100A9" "RNASE2"
[25] "VCAN" "LYZ" "SAT2" "QPCT" "CEBPD" "FCGR1A"
[1] ""
[1] "FCGR3A" "CDKN1C" "MS4A7" "HES4"
[5] "CKB" "RP11-290F20.3" "RHOC" "CTSL"
[9] "MS4A4A" "LILRA3" "SIGLEC10" "CTD-2006K23.1"
[13] "IFITM3" "HMOX1" "ABI3" "LRRC25"
[17] "IFITM2" "LILRB1" "BATF3" "PTP4A3"
[21] "CEBPB" "PILRA" "CSF1R" "HCK"
[25] "CXCL16" "VMO1" "C1QA" "TPPP3"
[29] "TCF7L2" "TNFSF10"
[1] ""
[1] ""
Seurat: Performing PCA
- Top 5 genes in the loading for PC1 and PC2.
# Can plot top genes for top components
PrintPCA(pbmc.seurat, pcs.print = 1:2, genes.print = 5, use.full = TRUE)
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[1] ""
[1] ""
Seurat: PCA Visualizations
- Top 30 genes associated with the first two components.
VizPCA(pbmc.seurat, pcs.use=1:2)
Seurat: PCA Visualizations
Seurat: Check your Batch Correction
How can we apply this?
- Can check components to see if gene show up that are enriched in components.
- Using the scores you can perform GE analysis to decribe the component.
Seurat: PCA Visualizations
PCAPlot(pbmc.seurat, 1, 2)
Seurat: PCA Visualizations
Seurat: PCA Visualizations
- Plot top 30 genes in top 100 cells for PC1.
PCHeatmap(pbmc.seurat, pc.use=1, cells.use=100, do.balanced=TRUE)
Seurat: PCA Visualizations
Seurat: Choosing Components
# Time Intensive
# Jackstraw
# pbmc.seurat <- JackStraw(pbmc.seurat, num.replicate = 100, do.print = FALSE)
Seurat: Choosing Components
How do we choose how many components to use?
- When there is diminishing returns to include it.
- Selection is performed more liberally in our setting.
# Scree (elbow) plot
PCElbowPlot(pbmc.seurat)
Seurat: Choosing Components
Seurat: Store Clusters
- Determine subclusters for the plot.
- Separate graph based approach.
- is not aware of the t-SNE projection.
- Using PC 1-10
# 1 minute
pbmc.seurat <- FindClusters(pbmc.seurat, pc.use = 1:10, resolution = 0.6, print.output = 0, save.SNN = TRUE)
Seurat: Run t-SNE
- Calculate and plot t-SNE on PC 1- 10.
- Uses Barnes-hut implementation.
# Calculate t-SNE Ordination
pbmc.seurat <- RunTSNE(pbmc.seurat, dims.use = 1:10, do.fast = TRUE)
# Plot
TSNEPlot(pbmc.seurat)
Seurat: Run t-SNE
This is not PCA
- Distance is best understood in close neighbors.
- The measurement of distance is difficult to understand.
Seurat: Side by side
PCA
t-SNE
Seurat: Plotting Genes Through Clusters
Now that we have subclusters of cell populations plotting genes through subclusters is identical to before.
- Seurat stores and groups by subclusters automatically.
VlnPlot(pbmc.seurat, c("MS4A1","CD79A"))
Seurat: Plotting Genes Through Clusters
Seurat: Plotting Genes on Clusters
You can also gene expression through out the cell ordination.
- Marker genes can help identify cell groups.
- Metadata can help visualize batch affects.
FeaturePlot(pbmc.seurat, c("MS4A1","CD3E", "GNLY", "FCER1A"), cols.use = c("grey","blue"))
Seurat: Plotting Genes on Clusters
Confirm Cell Identity
It is important to know cells are expressing expected genes.
- Many cell populations have known expressing genes.
- Many assays use sorting.
- Could have contaminants or unexpected diversity.
QC the Clusters!
FeaturePlot(pbmc.seurat, c("nGene"), cols.use = c("grey","blue"))
QC the Clusters!
QC the Clusters!
Check for your batch affect.
- We are going to make a fake batch affect (site) and plot this as an example of how one can visualize unwanted signal.
# Making Fake Data
fake.sites <- as.integer(pbmc.seurat@ident %in% c(5,2,8,7))
names(fake.sites) <- colnames(pbmc.seurat@data)
# Add metadata
pbmc.seurat <- AddMetaData(pbmc.seurat, fake.sites, "site")
# Plot feature
FeaturePlot(pbmc.seurat, c("site"), cols.use = c("green","orange"))
QC the Clusters!
Seurat: Getting your labels
cell.labels <- pbmc.seurat@ident
corner(cell.labels)
Seurat: Getting your labels
AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG AAACCGTGTATGCG
0 2 0 1 5
Levels: 0 1 2 3 4 5 6 7
Section Summary
- Dimensionality reduction help reduce data while hopefully keeping important signal.
- t-SNE on PCA is often used in analysis
- Created several types of plot often seen in publications.
- Plotting genes (through subgroups).
- Ordinating cells in t-SNE space.
- Heat maps of genes associated with PC components.
- Plotting metadata on projects of data is an important QC tool.
Where are We at Now?
- Can plot genes vs genes, and throughout cells.
- Can plot cells.
- Can break plotted cells into subgroups.
- These can become labels for later DE.
- Can plot across subgroups (maker genes).
- We can use plots of cells for batch affect visualization.
Differential Expression
Seurat: Differential Expression
- Default if one cluster again many tests.
- Can specify an ident.2 test between clusters.
- Adding speed by exluding tests.
- Min.pct - controls for sparsity
- Min percentage in a group
- Thresh.test - must have this difference in averages.
cluster1.markers <- FindMarkers(pbmc.seurat, ident.1 = 1, min.pct = 0.25)
head(cluster1.markers, 5)
Seurat: Differential Expression
- Find all cluster against all others.
pbmc.markers <- FindAllMarkers(pbmc.seurat, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
pbmc.markers %>% group_by(cluster) %>% top_n(2, avg_diff)
Source: local data frame [16 x 6]
Groups: cluster [8]
p_val avg_diff pct.1 pct.2 cluster gene
<dbl> <dbl> <dbl> <dbl> <fctr> <chr>
1 0 1.148598 0.925 0.482 0 LDHB
2 0 1.064824 0.662 0.202 0 IL7R
3 0 3.822197 0.996 0.217 1 S100A9
4 0 3.784692 0.975 0.123 1 S100A8
5 0 2.977214 0.936 0.042 2 CD79A
6 0 2.485397 0.623 0.022 2 TCL1A
7 0 2.171555 0.974 0.230 3 CCL5
8 0 2.113108 0.588 0.050 3 GZMK
9 0 2.268441 0.962 0.137 4 FCGR3A
10 0 2.150098 1.000 0.316 4 LST1
11 0 3.763449 0.961 0.131 5 GNLY
12 0 3.333932 0.955 0.068 5 GZMB
13 0 2.735429 0.861 0.009 6 FCER1A
14 0 2.013750 0.944 0.208 6 HLA-DQA1
15 0 5.872560 1.000 0.023 7 PPBP
16 0 4.949401 1.000 0.010 7 PF4
Seurat: DE Tests
bimod: Tests differences in mean and proportions.
roc: Uses AUC like definition of separation.
t: Student's T-test.
tobit: Tobit regression on a smoothed data.
Seurat: Plotting DE Genes
pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_diff) -> top10
DoHeatmap(pbmc.seurat, genes.use = top10$gene, order.by.ident = TRUE, slim.col.label = TRUE, remove.key = TRUE)
Seurat: Plotting DE Genes
pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_diff) -> top10
DoHeatmap(pbmc.seurat, genes.use = top10$gene, order.by.ident = TRUE, slim.col.label = TRUE, remove.key = TRUE)
SCDE: What is the Data?
- Data is from Islam et al. 2011
- Approximately 100 samples.
- Caution we use less…
- Mouse cells
- Embryonic fibroblasts (MEF; control)
- Embryonic stem cells (ES)
SCDE: Single Cell Differential Expression
SCDE: in Quick Theory
For each group (ES or MEF).
- Genes are modeled to have two groups of counts.
- Noisy area highly prone to dropout (Poisson distribution).
- “Amplified” signal (Negative Binomial distribution).
- This makes the error model to understand how much one can trust counts.
- Pairwise within groups.
Differential Expression.
- Expected value * the probability of dropout in that cell for that expression level.
SCDE: Load Data
- Pull in the data for the tutorial.
- In the future you would read in the data.
- Read.table or load Seurat object.
data(es.mef.small)
SCDE: Filter
- Filter the noise.
- Almost every package has a function for this.
- Filtering on gene prevalence.
dim(es.mef.small)
[1] 14897 40
cd <- clean.counts(es.mef.small, min.lib.size=1000, min.reads = 1, min.detected = 1)
dim(cd)
[1] 12142 40
SCDE: Create Labels
- Let's labels for the two groups.
## Setting up cells groups
data.groups <- rep(NA, ncol(es.mef.small))
data.groups[ grep("MEF", names(es.mef.small)) ] <- "MEF"
data.groups[ grep("ESC", names(es.mef.small)) ] <- "ESC"
data.groups <- factor(data.groups, levels = c("ESC","MEF"))
names(data.groups) <- colnames(es.mef.small)
table(data.groups)
data.groups
ESC MEF
20 20
SCDE: Calculate Error Models
- Some steps in SCDE can be time intensive.
- For the tutorial we will read in the result of this step.
## Calculate error models
## Time Intensive step
# o.ifm <- scde.error.models(counts=cd, groups= data.groups, n.cores=4,
# threshold.segmentation=TRUE, save.crossfit.plots=FALSE,
# save.model.plots=FALSE, verbose=1)
## Precomputed
data(o.ifm)
SCDE: Calculate Error Models
- How do we speed this up?
## Calculate error models
## Time Intensive step
# o.ifm <- scde.error.models(counts=cd, groups= data.groups, n.cores=4,
# threshold.segmentation=TRUE, save.crossfit.plots=FALSE,
# save.model.plots=FALSE, verbose=1)
# Check number of cores
detectCores()
[1] 8
SCDE: Model Details
# Error model coefficients (cells = rows)
# corr.a = slope of the correlated component fit
# Negative corr.a could be bad cells
# corr.b intercept of the correlated component fit
# corr.theta is NB over-dispersion
# fail.r background poisson rate
head(o.ifm)
SCDE: Model Details
conc.b conc.a fail.r corr.b corr.a corr.theta
ESC_10 -1.449443 0.5639140 -2.302585 0.7148157 0.6496142 0.7732069
ESC_11 -3.244421 0.7327046 -2.302585 1.5918205 0.5351960 0.7070433
ESC_12 -4.472559 0.8073935 -2.302585 1.5203470 0.4909147 0.7372590
ESC_13 -5.208909 0.8804523 -2.302585 1.2539230 0.5242493 0.8215473
ESC_14 -4.124369 0.7794612 -2.302585 1.1127353 0.5620266 0.7456712
ESC_15 -5.410838 0.9324758 -2.302585 1.1571732 0.5482784 0.7712750
SCDE: Filter Out Poor Fits
dim(o.ifm)
[1] 40 6
valid.cells <- o.ifm$corr.a > 0
table(valid.cells)
valid.cells
TRUE
40
o.ifm <- o.ifm[valid.cells, ]
dim(o.ifm)
[1] 40 6
SCDE: Estimate a Prior to Start
- Bayesian technique
- Need a starting point or first good guess to start.
## Calculate the Prior (starting value)
o.prior <- scde.expression.prior(models=o.ifm, counts=cd, length.out=400, show.plot=FALSE)
SCDE: Perform Differential Tests
- Why are we doing this again?
## Setting up cells groups
data.groups <- rep(NA, nrow(o.ifm))
data.groups[ grep("MEF", rownames(o.ifm)) ] <- "MEF"
data.groups[ grep("ESC", rownames(o.ifm)) ] <- "ESC"
data.groups <- factor(data.groups, levels = c("ESC","MEF"))
names(data.groups) <- row.names(o.ifm)
## Perform T-test like analysis
# 2 minutes on standard computer, 1 core
#ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups=data.groups, n.randomizations=100, n.cores=2, verbose=1)
load("data/ediff.Robj")
Visualize Differentially Expressed Genes
- mle = log2 fold change (estimate).
- ub and lb = upper and lower bound on mle.
- ce = log2 fold change (conservative estimate).
- Z = Z-score .
- cZ = Z-score corrected for multiple hypothesis testing.
SCDE: Top Upregulated Genes
- What are the top upregulated genes?
head(ediff[order(ediff$Z, decreasing = TRUE), ])
lb mle ub ce Z cZ
Dppa5a 8.075220 9.984631 11.575807 8.075220 7.160813 5.989598
Pou5f1 5.370220 7.200073 9.189043 5.370220 7.160328 5.989598
Gm13242 5.688455 7.677425 9.785734 5.688455 7.159979 5.989598
Tdh 5.807793 8.075220 10.302866 5.807793 7.159589 5.989598
Ift46 5.449779 7.359190 9.228822 5.449779 7.150242 5.989598
4930509G22Rik 5.409999 7.478528 9.785734 5.409999 7.115605 5.978296
SCDE: Write Data to Text File
- Save your data.
write.table(ediff[order(abs(ediff$Z), decreasing = TRUE), ],
file = "data/scde_results.txt", row.names = TRUE, col.names = TRUE, sep = "\t", quote = FALSE)
SCDE: Plot a Gene
- Let's look at our data.
scde.test.gene.expression.difference("Tdh", models = o.ifm, counts = cd, prior = o.prior)
SCDE: Plot a Gene
lb mle ub ce Z cZ
Tdh 5.728235 8.03544 10.30287 5.728235 7.151425 7.151425
SCDE: Interactive Exploration
- Interactive exploration.
# scde.browse.diffexp(ediff, o.ifm, cd, o.prior, groups = groups, name = "diffexp1", port = 1299)
SCDE and Batch Affects
- Create fake metadata.
batch <- as.factor(ifelse(rbinom(nrow(o.ifm), 1, 0.5) == 1, "batch1", "batch2"))
table(groups, batch)
SCDE and Batch Affects
scde.test.gene.expression.difference("Tdh", models = o.ifm, counts = cd, prior = o.prior, batch = batch)
SCDE and Batch Affects
- Here we correct initially instead of per gene.
ediff.batch <- scde.expression.difference(o.ifm, cd, o.prior, groups = groups, batch = batch, n.randomizations = 100, n.cores = 1, return.posteriors = TRUE, verbose = 1)
Pathway analysis
Pagoda
Pagoda: Overview Methodology
Fan et al.
Pagoda: What is the Data?
- Data being used is Pollen et al. 2014
- Approximately 300 samples.
- We focus on 64 samples (human, cortex)
- NPC - Neuronal Progenitor Cells
- GW16 - Gestational week 16
- GW21 - Gestational week 21
- GW21+3 - Gestational week 21 + 3 weeks.
Pagoda: Load Data
data(pollen)
# Original genes and cells (count matrix)
dim(pollen)
[1] 23710 64
# Filter poor cells
pollen.clean <- clean.counts(pollen)
# Cleaned matrix dimensions
dim(pollen.clean)
[1] 11310 64
Pagoda: Create a Color Legend
name.keys <- gsub("^Hi_(.*)_.*", "\\1", colnames(pollen.clean))
name.keys
[1] "NPC" "NPC" "NPC" "NPC" "NPC" "NPC" "NPC"
[8] "NPC" "NPC" "NPC" "NPC" "NPC" "NPC" "NPC"
[15] "NPC" "GW16" "GW16" "GW21" "GW21+3" "GW21+3" "GW16"
[22] "GW21+3" "GW21+3" "GW16" "GW16" "GW16" "GW16" "GW16"
[29] "GW16" "GW16" "GW16" "GW16" "GW16" "GW21" "GW21"
[36] "GW16" "GW16" "GW21" "GW16" "GW16" "GW16" "GW16"
[43] "GW16" "GW21" "GW21" "GW21" "GW21+3" "GW16" "GW16"
[50] "GW16" "GW16" "GW16" "GW21" "GW21+3" "GW21+3" "GW21+3"
[57] "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3"
[64] "GW21+3"
Pagoda: Create a Color Legend
l2cols <- c("coral4", "olivedrab3", "skyblue2", "slateblue3")[as.integer(factor(name.keys,
levels = c("NPC", "GW16", "GW21", "GW21+3")))]
l2cols
[1] "coral4" "coral4" "coral4" "coral4" "coral4"
[6] "coral4" "coral4" "coral4" "coral4" "coral4"
[11] "coral4" "coral4" "coral4" "coral4" "coral4"
[16] "olivedrab3" "olivedrab3" "skyblue2" "slateblue3" "slateblue3"
[21] "olivedrab3" "slateblue3" "slateblue3" "olivedrab3" "olivedrab3"
[26] "olivedrab3" "olivedrab3" "olivedrab3" "olivedrab3" "olivedrab3"
[31] "olivedrab3" "olivedrab3" "olivedrab3" "skyblue2" "skyblue2"
[36] "olivedrab3" "olivedrab3" "skyblue2" "olivedrab3" "olivedrab3"
[41] "olivedrab3" "olivedrab3" "olivedrab3" "skyblue2" "skyblue2"
[46] "skyblue2" "slateblue3" "olivedrab3" "olivedrab3" "olivedrab3"
[51] "olivedrab3" "olivedrab3" "skyblue2" "slateblue3" "slateblue3"
[56] "slateblue3" "slateblue3" "slateblue3" "slateblue3" "slateblue3"
[61] "slateblue3" "slateblue3" "slateblue3" "slateblue3"
Pagoda: Create Error Models
- This is a time intensive step.
- For now we are going to read in the data.
# knn <- knn.error.models(pollen.clean, k=ncol(pollen.clean)/4, n.cores=2,
# min.count.threshold=2, min.nonfailed=5, max.model.plots=10) Precomputed
# data
data(knn)
Pagoda: Normalize the Variance
# varinfo <- pagoda.varnorm(knn, counts=pollen.clean,
# trim=3/ncol(pollen.clean), max.adj.var=5, n.cores=1, plot=TRUE)
load("data/varinfo.Robj")
# list top overdispersed genes
sort(varinfo$arv, decreasing = TRUE)[1:10]
# Control for complexity
varinfo <- pagoda.subtract.aspect(varinfo, colSums(pollen.clean[, rownames(knn)] >
0))
Pagoda: Normalize the Variance
# varinfo <- pagoda.varnorm(knn, counts=pollen.clean,
# trim=3/ncol(pollen.clean), max.adj.var=5, n.cores=2, plot=TRUE)
load("data/varinfo.Robj")
Pagoda: Normalize the Variance
Pagoda: Normalize the Variance
# list top overdispersed genes
sort(varinfo$arv, decreasing = TRUE)[1:10]
DCX EGR1 FOS IGFBPL1 MALAT1 MEF2C STMN2 TOP2A
5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000
BCL11A SOX4
4.739497 4.489101
# Control for complexity
varinfo <- pagoda.subtract.aspect(varinfo, colSums(pollen.clean[, rownames(knn)] >
0))
Pagoda: Load Precomputed Gene Groups
- This analysis path relies on known gene groups.
library(org.Hs.eg.db)
# translate gene names to ids
ids <- unlist(lapply(mget(rownames(pollen.clean), org.Hs.egALIAS2EG, ifnotfound = NA),
function(x) x[1]))
rids <- names(ids)
names(rids) <- ids
Pagoda: Load Precomputed Gene Groups
# convert GO lists from ids to gene names
gos.interest <- unique(c(ls(org.Hs.egGO2ALLEGS)[1:100], "GO:0022008", "GO:0048699",
"GO:0000280", "GO:0007067"))
go.env <- lapply(mget(gos.interest, org.Hs.egGO2ALLEGS), function(x) as.character(na.omit(rids[x])))
go.env <- clean.gos(go.env) # remove GOs with too few or too many genes
go.env <- list2env(go.env) # convert to an environment
Pagoda: Known Gene Groups Overdispersion
# pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components = 1, n.cores =
# 1)
load("data/pwpca.Robj")
df <- pagoda.top.aspects(pwpca, return.table = TRUE, plot = TRUE, z.score = 1.96)
Pagoda: Known Gene Groups Overdispersion
Pagoda: Known Gene Groups Overdispersion
head(df)
Pagoda: Novel Gene Group Overdispersion
- Unsupervised path.
# clpca <- pagoda.gene.clusters(varinfo, trim = 7.1/ncol(varinfo$mat),
# n.clusters = 50, n.cores = 1, plot = TRUE)
load("data/clpca.Robj")
Pagoda: Novel Gene Group Overdispersion
Pagoda: Novel Gene Group Overdispersion
df <- pagoda.top.aspects(pwpca, clpca, return.table = TRUE, plot = TRUE, z.score = 1.96)
Pagoda: Novel Gene Group Overdispersion
Pagoda: Novel Gene Group Overdispersion
head(df)
Pagoda: Novel Gene Group Overdispersion
name npc n score z adj.z sh.z adj.sh.z
56 geneCluster.6 1 391 3.289412 13.814757 13.530139 NA NA
49 GO:0000280 1 428 1.587359 11.592060 11.252131 NA NA
50 GO:0007067 1 362 1.585331 10.873998 10.576419 NA NA
79 geneCluster.29 1 171 1.583733 5.162516 4.523195 NA NA
14 GO:0000070 1 116 1.446979 5.625549 5.247594 NA NA
89 geneCluster.39 1 178 1.392980 4.137589 3.438496 NA NA
Pagoda: Cell Clustering
# Get full info on the top aspects tam <- pagoda.top.aspects(pwpca, clpca,
# n.cells = NULL, z.score = qnorm(0.01/2, lower.tail = FALSE))
load("data/tam.Robj")
# Determine overall cell clustering
hc <- pagoda.cluster.cells(tam, varinfo)
# tamr <- pagoda.reduce.loading.redundancy(tam, pwpca, clpca)
load("data/tamr.Robj")
# tamr2 <- pagoda.reduce.redundancy(tamr, distance.threshold = 0.9, plot =
# TRUE, cell.clustering = hc, labRow = NA, labCol = NA, box = TRUE, margins
# = c(0.5, 0.5), trim = 0)
load("data/tamr2.Robj")
col.cols <- rbind(groups = cutree(hc, 3))
Pagoda: Visualize Clustering
pagoda.view.aspects(tamr2, cell.clustering = hc, box = TRUE, labCol = NA, margins = c(0.5,
20), col.cols = rbind(l2cols))
Pagoda: Visualize Clustering
Pagoda: Interactive Clustering
## compile a browsable app, showing top three clusters with the top color bar
## app <- make.pagoda.app(tamr2, tam, varinfo, go.env, pwpca, clpca, col.cols
## = col.cols, cell.clustering = hc, title = 'NPCs') show app in the browser
## (port 1468) show.app(app, 'pollen', browse = TRUE, port = 1468)
Section Summary
- Generated groups of cells (labels) from subclusters.
- DE can be performed on cell subclusters.
- Addresses the specific characteristics of the underlying data.
- Is a t-test not an ANOVA
- Can be time intensive.
- Pathway enrichment analysis can be performed using de novo and established gene groupings.
What Did We Miss? (So Much)
- Seurat
- Data imputation, spatial inference, batch effect correction, …
- SCDE
- Batch Effect Correction, …
- Pseudotemporal Analysis (time-series)
- Monocle2, …
- So many more tools are emerging, … – https://github.com/seandavi/awesome-single-cell
What did we accomplish?
- Looked at the data and performed filtering and QC
- Plot genes and cells.
- Discovered subgroups of cells.
- Groups of cells were used as labels in DE.
- Groups of genes were analyzed for enrichment.
Thank You
- Aviv Regev
- Brian Haas
- Anindita Basu
- Asma Bankapur
- Chloe Villani
- Karthik Shekhar
- Matan Hofree
- Michel Cole
- Monika Kowalczyk
- Nir Yosef
- Regev Single Cell Working Group
- Today's Attendees
Questions?
Notes: to Make a PDF
- Create a pdf file before you plot ( can plot multiple plots ).
- Close the plotting.
# pdf( 'data/my_file.pdf', useDingbats = FALSE ) # Start pdf plot( 1:10,
# log(1:10 ) ) # plot in to the pdf file plot( seq(0,.9,.1), sin(0:9) ) #
# another plot for the pdf file dev.off() # Close pdf file ( very important
# )